This is achieved by using machine teaching tools, such as conversation learning (CL) [1]}. After the deployment of a system, CL stores user-system dialog sessions in the logs and selects a set of representative (failed) dialog sessions for dialog authors to teach the system to correct its behaviors so that the system can successfully complete the same or similar tasks next time. Figure REF  illustrates the machine teaching process using CL.
It is also crucial to avoid catastrophic forgetting [2]}, [3]}
in which the system improved using new dialog logs forgets how to deal with previously successfully handled tasks. This can be prevented by storing a collection of dialog sessions generated by previous versions of the system that are considered representative (the regression dataset). By always re-training the system using the regression dataset we can ensure that the bot learns new skills without forgetting previously learned skills.
But how to select dialog sessions for the regression set and how to use the regression set for machine teaching are open research problems. One solution is to store a machine-learned generative model that can re-generate a regression set for system training and evaluation, instead of storing large amounts of previous dialog sessions.
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